1. Field of the Invention
The present invention relates to a digital image processing technique, and more particularly to a method and apparatus for processing and segmenting areas in images.
2. Description of the Related Art
Mammography images are powerful tools used in the diagnosis of medical problems of breasts. An important feature in mammography images is the pectoral muscle area. The pectoral muscle area can be used to identify breast abnormalities, can facilitate comparative analysis and processing of mammography images, can convey significant information relating to breast position and deformation, etc.
For example, the pectoral muscle in the medio-lateral view, as is typically shown in mammograms, is a major landmark for mammogram reconstruction and registration. The pectoral muscle also helps quantify quality of mammograms and can be used for automatic quality assurance. The area overlying the pectoral muscle is a highly suspicious zone for development of masses and is checked by radiologists for false-negatives (non-cancerous areas). Also, mammographic parenchymal patterns, which are a marker of breast cancer risk, and the pectoral muscle have identical texture, which leads to false-positives in detection of malignant masses.
Pectoral muscle identification is a non-trivial task due to variability of borders and contrast, and unclear areas in breast images. Typical/conventional methods to detect the pectoral muscle rely on some heuristic gradient measures to fit a line to the pectoral muscle boundary. One such pectoral muscle detection technique is described in the publication “Automatic Pectoral Muscle Segmentation on Mediolateral Oblique View Mammograms”, by S. Kwok, R. Chandrasekhar, Y. Attikiouzel and M. Rickard, IEEE Transactions on Medical Imaging v.23 No. 9, September 2004. In the technique described in this publication, an adaptive algorithm is proposed to automatically extract the pectoral muscle in digitized mammograms. The adaptive algorithm uses knowledge about the position and shape of the pectoral muscle on medio-lateral oblique views. The pectoral edge is first estimated by a straight line that is validated for correctness of location and orientation. The linear estimate is then refined using iterations. This technique, however, relies on detailed pre-operational knowledge of the digital data, to tune algorithms for the data at hand, and requires adjustments of many parameters. This technique force-fits a line or curve to the pectoral muscle boundary, even though the boundary may not be amenable to curve fitting.
Disclosed embodiments of this application address these and other issues by using a method and an apparatus for pectoral muscle detection using cluster-modified graph cuts. The method and apparatus segment graphs associated with breast images, to obtain pixels associated with the pectoral muscle. The method and apparatus segment graphs associated with breast images by incorporating clustering results for pixels into graph segmentation. The method and apparatus are applicable to breast images with various views, and do not require tuning beforehand. The method and apparatus are applicable to other types of images besides breast images, and detect various objects included in images.